Evidence map›Paper›PMID 39860825›Full record

ArticleSensors (Basel, Switzerland)2025

Developing a Health Support System to Promote Care for the Elderly.

Marcell Szántó, Lehel Dénes-Fazakas, Erick Noboa, Levente Kovács, Döníz Borsos, György Eigner, Éva-H Dulf

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Marcell SzántóBiomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.
Lehel Dénes-FazakasBiomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.ORCID 0009-0003-9879-5664
Erick NoboaApplied Informatics and Applied Mathematics Doctoral School, Obuda University, 1034 Budapest, Hungary.ORCID 0009-0005-3681-3569
Levente KovácsBiomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.ORCID 0000-0002-3188-0800
Döníz BorsosInstitute of Instrumentation and Automation, Kandó Kálmán Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary.ORCID 0000-0003-2678-6585
György EignerBiomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.ORCID 0000-0001-8038-2210
Éva-H DulfPhysiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.

Funding

National Research, Development and Innovation Fund of Hungary 2019-1.3.1-KK-2019-00007National Research, Development, and Innovation Fund of Hungary TKP2021-NKTA-36New National Excellence 397 Program of the Ministry for Innovation and Technology ÚNKP-21-3Staring Researcher Program of Óbuda University Staring Researcher Program of Óbuda University
6 · The paper itself

Abstract

In light of the demographic shift towards an aging population, there is an increasing prevalence of dementia among the elderly. The negative impact on mental health is preventing individuals from taking proper care of themselves. For individuals requiring hospital care, those receiving home care, or as a precaution for a specific individual, it is advantageous to utilize monitoring equipment to track their biological parameters on an ongoing basis. This equipment can minimize the risk of serious accidents or severe health hazards. The objective of the present research project is to design an armband with an accurate location tracking system. This is of particular importance for individuals with dementia and Alzheimer's disease, who frequently leave their homes and are unable to find their way back. The proposed armband also includes a fingerprint identification system that allows only authorized personnel to use it. Furthermore, in hospitals and healthcare facilities the biometric identification system can be used to trace periodic medical or nursing visits. This process improves the reliability and transparency of healthcare. The test results indicate that the armband functions in accordance with the desired design specifications, with performance evaluation of the main features including fall detection, where a hit rate of 100% was obtained, a fingerprint recognition test demonstrating accuracy from 88% to 100% on high-quality samples, and a GPS tracking test determining position with a difference of between 1.8 and 2.1 m. The proposed solution may be of benefit to healthcare professionals, supported housing providers, elderly people as target users, or their family members.

Indexed as

Monitoring, PhysiologicAccidental FallsAgedAged, 80 and overAlzheimer DiseaseDementiaGeographic Information SystemsHumansAlzheimer’s diseasebiometrics identificationdementiaelderlyfall detectionhealthcareLoRaWANPPGsafety

Identifiers

PMID39860825
PMCPMC11769229

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.